Radio frequency measurements utilizing cavities and waveguides may be used in a wide range of process control systems, to monitor the state of the system, detect faults, and provide adaptive feedback control to optimize the process. Microwave cavity and waveguide measurements are useful to provide information on the state of the system in situ, without the need for sample removal and subsequent analysis, as is the case with many existing systems.
Examples to illustrate the broad applicability of radio frequency-based cavity and transmission measurement systems include: engines and engine systems, power plants, chemical plants, petroleum extraction and processing, and process sensing and controls in any number of systems.
Current sensing and control networks for process control systems suffer from a number of limitations, which are briefly summarized as follows:
First, in many systems there is a need to physically remove a sample from a discrete point in the system at specified time intervals in order to subsequently analyze the sample. These measurements incur a time delay between the time when the sample is collected and when the sample is analyzed, which may range from a few minutes to weeks or even months in some cases. The process of removing the sample may introduce additional variability in the measurements, which may be related to sample handling, the sampling method employed, and the location and timing of the sample extraction, among others. In addition to introducing potential for added variability, measurements based on extracted samples provide limited information corresponding only to the sample characteristics or state at the time of sample extraction from the system. The time delay between sample collection and receipt of measurement results does not allow for efficient process optimization or detection of faults or error conditions when they occur.
Second, many processes employ sensors to monitor the state or characteristics of various system parameters in-line. Examples of these types of sensors include temperature sensors, pressure sensors, moisture sensors, composition sensors such as gas sensors, particle sensors, and similar sensors. Most of these sensors, however, only provide a measurement of the process parameters in close proximity to the sensor or require close contact between the material being measured and the sensing element itself. Use of these types of sensors greatly restricts the type of parameters which may be directly monitored, and also limits the measurements to discrete points in the system where the sensors are located.
Third, in order to measure various different characteristics of a system, many different types of sensors are generally required, each employing a different measurement principle. For example, temperature, pressure, and gas composition sensors (oxygen, NOx, ammonia, PM) may be used in an exhaust system. Use of many different types of sensors, each with their own specific requirements and response characteristics, increases the cost and complexity of sensing and control networks.
Fourth, despite the prevalence of a large number of sensors, oftentimes the actual state variable of interest may not be measured directly, and must be indirectly estimated based on measurements from available sensors. For example, the amount of material accumulated on a filter may be inferred from pressure drop measurements across the filter, or the amount of a gas adsorbed on a catalyst may be inferred from gas composition sensors monitoring gas composition upstream or downstream of the catalyst. In another example, measurements of upstream and downstream process parameters may be used to infer or indirectly detect a failure of malfunction of a device, such as a filter or catalyst, using conventional sensors. However, in these cases, direct measurement of the required state variable, namely the amount of material on the filter or the quantity of a species adsorbed on a catalyst can not be measured directly. Such indirect estimates suffer from poor accuracy, and are cumbersome and time-consuming to calibrate.
Fifth, in many cases, there is a need to detect system faults or malfunctions when they occur, or preferentially to detect signs of faults or malfunctions before they occur. In particular, certain components in the system may mask signs of faults or malfunctions making them difficult to detect through conventional sensing means. For example, exhaust particulate filters may mask observable signs of impending engine faults, such as smoke related to high oil or fuel consumption or water vapor due to a coolant leak. Such faults are difficult to detect using conventional sensors, or may be easily mistaken or confused, using measurements from conventional sensors.
Sixth, many conventional sensors such as electrochemical gas sensors, accumulation type soot or particle sensors, and the like require contact or direct interaction of the sensing element with the material being measured. Such sensors suffer from fouling, poisoning, or aging through the build-up of contaminant material on the sensing element, which needs to be avoided.
It is, thus, desired to have an improved sensing and control network. Such an improved network may exhibit one or more of the following attributes: (i) direct measurement of the state variable or variables of interest, (ii) in-situ measurements, (iii) fast response time, (iv) the ability to sample a multiple large volumes (i.e., selectively choosing the region in the device that is being sampled) and/or detect changes in the system which may not be in close proximity to the sensing element, (v) improved measurement accuracy and feedback control, (vi) non-contact sensing methods whereby the sensing element does not need to come in contact with the material or processes being interrogated, and (vii) a simplified and less cumbersome measurement system.
It is further desirable to measure the deposition of materials on surfaces of process systems, such as walls of the device or reactor, in one example, that are detrimental to the operation of the device (such as deposits on cladding of furnaces or biofilms in chemical reactors).
Therefore, an improved process sensing and controls network is needed, which will have considerable utility for a broad range of applications and fields of uses.